Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review
Artificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Urban Science |
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| Online Access: | https://www.mdpi.com/2413-8851/9/6/202 |
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| author | Bruno Palley João Poças Martins Hermano Bernardo Rosaldo Rossetti |
| author_facet | Bruno Palley João Poças Martins Hermano Bernardo Rosaldo Rossetti |
| author_sort | Bruno Palley |
| collection | DOAJ |
| description | Artificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have been applied in several fields. In this context, combining Digital Twins, Machine Learning, and Smart Buildings offers significant potential to improve energy efficiency and operational effectiveness in building management. This review aims to identify and analyze studies that explore the application of Machine Learning and Digital Twins for operation and energy management in Smart Buildings, providing an updated perspective on these rapidly evolving topics. The methodology follows the PRISMA guidelines for systematic reviews, using Scopus and Web of Science databases. This review identifies the main concepts, objectives, and trends emerging from the literature. Furthermore, the findings confirm the recent growth in research combining Machine Learning and Digital Twins for building management, revealing diverse approaches, tools, methods, and challenges. Finally, this paper highlights existing research gaps and outlines opportunities for future investigation. |
| format | Article |
| id | doaj-art-40dc4dc2c2bc4942baf5d93ff20291aa |
| institution | Kabale University |
| issn | 2413-8851 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Urban Science |
| spelling | doaj-art-40dc4dc2c2bc4942baf5d93ff20291aa2025-08-20T03:26:56ZengMDPI AGUrban Science2413-88512025-06-019620210.3390/urbansci9060202Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic ReviewBruno Palley0João Poças Martins1Hermano Bernardo2Rosaldo Rossetti3INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalCONSTRUCT—Gequaltec, Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalLIACC—Artificial Intelligence and Computer Science Laboratory, Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalArtificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have been applied in several fields. In this context, combining Digital Twins, Machine Learning, and Smart Buildings offers significant potential to improve energy efficiency and operational effectiveness in building management. This review aims to identify and analyze studies that explore the application of Machine Learning and Digital Twins for operation and energy management in Smart Buildings, providing an updated perspective on these rapidly evolving topics. The methodology follows the PRISMA guidelines for systematic reviews, using Scopus and Web of Science databases. This review identifies the main concepts, objectives, and trends emerging from the literature. Furthermore, the findings confirm the recent growth in research combining Machine Learning and Digital Twins for building management, revealing diverse approaches, tools, methods, and challenges. Finally, this paper highlights existing research gaps and outlines opportunities for future investigation.https://www.mdpi.com/2413-8851/9/6/202machine learningdigital twinssmart buildingsenergy management |
| spellingShingle | Bruno Palley João Poças Martins Hermano Bernardo Rosaldo Rossetti Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review Urban Science machine learning digital twins smart buildings energy management |
| title | Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review |
| title_full | Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review |
| title_fullStr | Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review |
| title_full_unstemmed | Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review |
| title_short | Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review |
| title_sort | integrating machine learning and digital twins for enhanced smart building operation and energy management a systematic review |
| topic | machine learning digital twins smart buildings energy management |
| url | https://www.mdpi.com/2413-8851/9/6/202 |
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